



'''simple docstring'''




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch



def       a     (					lowerCamelCase__       ):  # picklable for multiprocessing
			'''simple docstring'''





			return x.sum()



def       a     (					lowerCamelCase__       ):  # picklable for multiprocessing
			'''simple docstring'''





			return i + 1







@dataclass
class       _lowerCAmelCase				:
	__SCREAMING_SNAKE_CASE  :						int
	__SCREAMING_SNAKE_CASE  :						str







class       _lowerCAmelCase				(     __snake_case					):


	def        _a      (self				):
				A_      :		str      =	{}
				A_      :		List[Any]      =	[]
				A_      :		int      =	1
				A_      :		Optional[int]      =	[1, 2]
				A_      :		Union[str, Any]      =	{"""a""": 1, """b""": 2}
				A_      :		Optional[Any]      =	{"""a""": [1, 2], """b""": [3, 4]}
				A_      :		List[Any]      =	{"""a""": {"""1""": 1}, """b""": 2}
				A_      :		Optional[int]      =	{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
				A_      :		Union[str, Any]      =	{}
				A_      :		int      =	[]
				A_      :		Tuple      =	2
				A_      :		Optional[Any]      =	[2, 3]
				A_      :		Union[str, Any]      =	{"""a""": 2, """b""": 3}
				A_      :		str      =	{"""a""": [2, 3], """b""": [4, 5]}
				A_      :		Tuple      =	{"""a""": {"""1""": 2}, """b""": 3}
				A_      :		str      =	{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)

				A_      :		List[Any]      =	2
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)

				A_      :		Optional[int]      =	{"""a""": np.eye(2				), """b""": np.zeros(3				), """c""": np.ones(2				)}
				A_      :		Union[str, Any]      =	{"""a""": 2, """b""": 0, """c""": 2}
				A_      :		Union[str, Any]      =	{
				    """a""": np.eye(2				).astype(__SCREAMING_SNAKE_CASE				),
				    """b""": np.zeros(3				).astype(__SCREAMING_SNAKE_CASE				),
				    """c""": np.ones(2				).astype(__SCREAMING_SNAKE_CASE				),
				}
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, map_numpy=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(
				    {k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, map_numpy=__SCREAMING_SNAKE_CASE				).items()}			, {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}			, )
				self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, map_numpy=__SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)			, __SCREAMING_SNAKE_CASE				)
				self.assertEqual(
				    {k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, map_numpy=__SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				).items()}			, {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}			, )
				with self.assertRaises(__SCREAMING_SNAKE_CASE				):  # can't pickle a local lambda
							map_nested(lambda lowercase				: x + 1			, __SCREAMING_SNAKE_CASE			, num_proc=__SCREAMING_SNAKE_CASE				)


	def        _a      (self				):
				A_      :		Any      =	{"""a""": 1, """b""": 2}
				A_      :		Optional[Any]      =	{"""a""": 3, """b""": 4}
				A_      :		Optional[int]      =	{"""a""": 5, """b""": 6}
				A_      :		Dict      =	sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]				)
				self.assertEqual(sorted(zip_dict(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)				)			, __SCREAMING_SNAKE_CASE				)


	def        _a      (self				):







				class       _lowerCAmelCase				:
					__SCREAMING_SNAKE_CASE  :						Tuple						     =   """bar"""






				A_      :		List[str]      =	Foo()
				self.assertEqual(foo.my_attr			, """bar"""				)
				with temporary_assignment(__SCREAMING_SNAKE_CASE			, """my_attr"""			, """BAR"""				):
							self.assertEqual(foo.my_attr			, """BAR"""				)
				self.assertEqual(foo.my_attr			, """bar"""				)






@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""		, [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]		, )
def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			with patch("""datasets.utils.py_utils._single_map_nested"""       ) as mock_single_map_nested, patch(
			    """datasets.parallel.parallel.Pool"""       ) as mock_multiprocessing_pool:
						A_      :		int      =	{f'{i}': i for i in range(_lowerCamelCase       )}
						A_      :		str      =	map_nested(lambda lowerCamelCase__       : x + 10		, _lowerCamelCase		, num_proc=_lowerCamelCase		, parallel_min_length=16       )
						if expected_num_proc == 1:
									assert mock_single_map_nested.called
									assert not mock_multiprocessing_pool.called
						else:
									assert not mock_single_map_nested.called
									assert mock_multiprocessing_pool.called
									assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc







class       _lowerCAmelCase				(     __snake_case					):


	@require_tf
	def        _a      (self				):
				import tensorflow as tf
				from tensorflow.keras import layers

				A_      :		Any      =	layers.Dense(2				)

				def gen_random_output():
							A_      :		Tuple      =	tf.random.uniform((1, 3)				)
							return model(__SCREAMING_SNAKE_CASE				).numpy()

				with temp_seed(42			, set_tensorflow=__SCREAMING_SNAKE_CASE				):
							A_      :		List[str]      =	gen_random_output()
				with temp_seed(42			, set_tensorflow=__SCREAMING_SNAKE_CASE				):
							A_      :		Dict      =	gen_random_output()
				A_      :		Tuple      =	gen_random_output()

				np.testing.assert_equal(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)
				self.assertGreater(np.abs(outa - outa				).sum()			, 0				)


	@require_torch
	def        _a      (self				):
				import torch

				def gen_random_output():
							A_      :		str      =	torch.nn.Linear(3			, 2				)
							A_      :		int      =	torch.rand(1			, 3				)
							return model(__SCREAMING_SNAKE_CASE				).detach().numpy()

				with temp_seed(42			, set_pytorch=__SCREAMING_SNAKE_CASE				):
							A_      :		List[Any]      =	gen_random_output()
				with temp_seed(42			, set_pytorch=__SCREAMING_SNAKE_CASE				):
							A_      :		Tuple      =	gen_random_output()
				A_      :		Union[str, Any]      =	gen_random_output()

				np.testing.assert_equal(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)
				self.assertGreater(np.abs(outa - outa				).sum()			, 0				)


	def        _a      (self				):
				def gen_random_output():
							return np.random.rand(1			, 3				)

				with temp_seed(42				):
							A_      :		Optional[int]      =	gen_random_output()
				with temp_seed(42				):
							A_      :		int      =	gen_random_output()
				A_      :		int      =	gen_random_output()

				np.testing.assert_equal(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE				)
				self.assertGreater(np.abs(outa - outa				).sum()			, 0				)






@pytest.mark.parametrize("""input_data"""		, [{}]       )
def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Tuple      =	NestedDataStructure(_lowerCamelCase       ).data
			assert output_data == input_data



@pytest.mark.parametrize(
    """data, expected_output"""		, [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]		, )
def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Optional[int]      =	NestedDataStructure(_lowerCamelCase       ).flatten()
			assert output == expected_output



def       a     (					):
			'''simple docstring'''





			A_      :		Union[str, Any]      =	A(x=1		, y="""foobar"""       )
			A_      :		Dict      =	{"""x""": 1, """y""": """foobar"""}
			assert asdict(_lowerCamelCase       ) == expected_output

			A_      :		Dict      =	{"""a""": {"""b""": A(x=10		, y="""foo"""       )}, """c""": [A(x=20		, y="""bar"""       )]}
			A_      :		Union[str, Any]      =	{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
			assert asdict(_lowerCamelCase       ) == expected_output

			with pytest.raises(_lowerCamelCase       ):
						asdict([1, A(x=10		, y="""foo"""       )]       )



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			return text.split()



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			yield (time.time(), content)
			time.sleep(2       )
			yield (time.time(), content)



def       a     (					):
			'''simple docstring'''





			with Pool(2       ) as pool:
						A_      :		Any      =	list(iflatmap_unordered(_lowerCamelCase		, _split_text		, kwargs_iterable=[{"""text""": """hello there"""}] * 10       )       )
						assert out.count("""hello"""       ) == 10
						assert out.count("""there"""       ) == 10
						assert len(_lowerCamelCase       ) == 20

			# check multiprocess from pathos (uses dill for pickling)
			with multiprocess.Pool(2       ) as pool:
						A_      :		Dict      =	list(iflatmap_unordered(_lowerCamelCase		, _split_text		, kwargs_iterable=[{"""text""": """hello there"""}] * 10       )       )
						assert out.count("""hello"""       ) == 10
						assert out.count("""there"""       ) == 10
						assert len(_lowerCamelCase       ) == 20

			# check that we get items as fast as possible
			with Pool(2       ) as pool:
						A_      :		Tuple      =	[]
						for yield_time, content in iflatmap_unordered(
						    _lowerCamelCase		, _aseconds_generator_of_aitems_with_timing		, kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]       ):
									assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
									out.append(_lowerCamelCase       )
						assert out.count("""a"""       ) == 2
						assert out.count("""b"""       ) == 2
						assert len(_lowerCamelCase       ) == 4







"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]=3			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=32			,		__SCREAMING_SNAKE_CASE	:				int=3			,		__SCREAMING_SNAKE_CASE	:				str=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=[10, 20, 30, 40]			,		__SCREAMING_SNAKE_CASE	:				List[Any]=[1, 1, 2, 1]			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				Tuple=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]="relu"			,		__SCREAMING_SNAKE_CASE	:				Tuple=3			,		__SCREAMING_SNAKE_CASE	:				Dict=None			,		)	->						Optional[int]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		embeddings_size
      lowerCamelCase_       	=		hidden_sizes
      lowerCamelCase_       	=		depths
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		num_labels
      lowerCamelCase_       	=		scope
      lowerCamelCase_       	=		len(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Dict:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.num_labels   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return RegNetConfig(
          num_channels=self.num_channels			,		embeddings_size=self.embeddings_size			,		hidden_sizes=self.hidden_sizes			,		depths=self.depths			,		hidden_act=self.hidden_act			,		num_labels=self.num_labels			,		)






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				int   )	->						List[Any]:
      lowerCamelCase_       	=		RegNetModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape			,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,		)






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Union[str, Any]:
      lowerCamelCase_       	=		self.num_labels
      lowerCamelCase_       	=		RegNetForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels)   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						str:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	int		=					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	int		=					(
        {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	int		=					False
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					False
    SCREAMING_SNAKE_CASE		:	int		=					False






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						List[Any]:
      lowerCamelCase_       	=		RegNetModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()






    def   UpperCamelCase			(			self	:				Any   )	->						Optional[Any]:
      return






    @unittest.skip(reason='RegNet does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				List[str]   )	->						Union[str, Any]:
      pass






    @unittest.skip(reason='RegNet does not support input and output embeddings'   )
    def   UpperCamelCase			(			self	:				Any   )	->						Dict:
      pass






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(config=__SCREAMING_SNAKE_CASE   )
        for name, module in model.named_modules():
          if isinstance(__SCREAMING_SNAKE_CASE			,		(nn.BatchNormad, nn.GroupNorm)   ):
            self.assertTrue(
                torch.all(module.weight == 1   )			,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,		)
            self.assertTrue(
                torch.all(module.bias == 0   )			,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      def check_hidden_states_output(__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]   ):
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()

        with torch.no_grad():
          lowerCamelCase_       	=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

        lowerCamelCase_       	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        lowerCamelCase_       	=		self.model_tester.num_stages
        self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		expected_num_stages + 1   )

        # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )			,		[self.model_tester.image_size // 2, self.model_tester.image_size // 2]			,		)

      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()
      lowerCamelCase_       	=		['basic', 'bottleneck']
      for model_class in self.all_model_classes:
        for layer_type in layers_type:
          lowerCamelCase_       	=		layer_type
          lowerCamelCase_       	=		True
          check_hidden_states_output(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCamelCase_       	=		True

          check_hidden_states_output(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		RegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							Optional[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[int]:
      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
          if is_vision_available()
          else None
      )






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict:
      lowerCamelCase_       	=		RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.4_180, -1.5_051, -3.4_836]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




